• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.114-123
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• 2017

This paper introduces a novel obstacle-climbing robot that moves on the facade of buildings and its climbing mechanism. A winch system set on the top of the building makes the vertical motion of the robot while it climbs obstacles that protrude from the wall surface. The obstacle-climbing robot suggested in this research is composed of a main platform and three modular climbing units. Various sensors installed on each climbing unit detect the obstacles, and the robot controller coordinates the three units and the winch to climb the obstacles using the obstacle-climbing mechanism. To evaluate the performance of the developed robot prototype, a test bed, which consists of an artificial wall and an obstacle, was manufactured. The obstacle size and the time required to climb the obstacle were selected as the performance indices, and extensive experiments were carried out. As a result, it was confirmed that the obstacle-climbing robot can climb various-sized obstacles with a reasonable speed while it moves on the wall surface.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.814-819
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• 2007

Palletizing task is well-known time consuming and laborious process in factory, hence automation is seriously required. To do this, artificial robot is generally used. These systems however, mostly user teaches the robot point to point and to avoid time-variable obstacle, robot is required to attach the vision camera. These system structures bring about inefficiency and additional cost. In this paper we propose task-oriented trajectory generation algorithm for palletizing. This algorithm based on $A^{*}$ algorithm and slice plane theory, and modify the object dealing method. As a result, we show the elapsed simulation time and compare with old method. This simulation algorithm can be used directly to the off-line palletizing simulator and raise the performance of robot palletizing simulator not using excessive motion area of robot to avoid adjacent components or vision system. Most of all, this algorithm can be used to low-level PC or portable teach pendent

• Journal of KSNVE
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• v.8 no.2
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• pp.239-250
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• 1998

Traditionally, intake noise from internal combustion engine has not recevied much attention compared to exhaust noise. But nowadays, intake noise is a major contributing factor to automotive passenger compartment noise levels. The main objective of this paper is to identify the mechanism of generation, propagation and radiation of the intake noise. With a simplest geometric model, one of the main noise sources for the intake stroke is found to be the pressure surge, which is generated after intake valve closing. The pressure surge, which has the nonlinear acoustic behavior, propagates and radiates with relatively large amplitude. In this paper, unsteady compressible Navier-Stokes equations are employed for the intake stroke of axisymmetric model having a single moving cylinder and a single moving intake valve. To simulate the periodic motion of the piston and the valve, unsteady deforming mesh algorithm is employed and Thompson's non-reflecting boundary condition is applied to the radiation field. In order to resolve the small amplitude waves at the radiation field, essentially non-oscillatory(ENO) schemes with an artificial compression method (ACM) are used.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.703-704
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• 2018

An artificial neural network design that traces the pupil for the disables suffering from Lou Gehrig disease is introduced. It grasps the position of the pupil required for the communication system. Tensorflow is used for generating and learning the neural network, and the pupil position is determined through the learned neural network. Convolution neural network(CNN) which consists of 2 stages of convolution layer and 2 layers of complete connection layer is implemented for the system.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.9
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• pp.52-59
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• 2009

In this paper, the planar waveguide slotted array antenna is presented, which has the 3-layered structure of feeding networks for a high gain. Due to the ionosphere which generates 'Faraday rotation', the skew is happened between the signal radiated from an artificial satellite and the receiving antenna. This causes a polarization loss. In this paper, to remove this polarization loss, the dumbbell shaped linear polarizer using a single layer film is proposed. The gain of proposed antenna is 29.4dB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1304-1311
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• 2003

Target tracking in a real world situation is difficult problem because of continuous variations in images, huge amounts of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.68-68
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• 2017

Measurements of solar wind speed near the Sun (< 0.1 AU) are important for understanding acceleration mechanism of solar wind as well as space weather predictions, but hard to directly measure them. For the first time, we provide 2D solar wind speed maps in the LASCO field of view using three consecutive days data. By applying the Fourier convolution and inverse Fourier transform, we decompose the 3D intensity data (r, PA, t) into the 4D one (r, PA, t, v). Then, we take the weighted mean along speed to determine the solar wind speeds that gives V(r, PA, t) in every 30 min. The estimated radial speeds are consistent with those given by an artificial flow and plasma blobs. We find that the estimated speeds are moderately correlated with those from slow CMEs and those from IPS observations. A comparison of yearly solar wind speed maps in 2000 and 2009 shows that they have very remarkable differences: azimuthally uniform distribution in 2000 and bi-modal distribution (high speed near the poles and low speed near the equator) in 2009.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.141-147
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• 2015

In this study, a position control algorithm for an omni-directional mobile robot based on Mecanum wheels was introduced and experimentally evaluated. Multiple ultrasonic sensors were installed around the mobile robot to obtain position feedback. Using the distance of the robot from the wall, the position and orientation of the mobile robot were calculated. In accordance with the omni-directional velocity generation mechanism, the velocity kinematics between the Mecanum wheel and the mobile platform were determined. Based on this formulation, a simple and intuitive position control algorithm was suggested. To evaluate the control algorithm, a test bed composed of artificial walls was designed and implemented. While conventional control algorithms based on normal wheels require additional path planning for two-dimensional planar motion, the omni-directional mobile robot using distance sensors was able to directly follow target positions with the simple proposed position feedback algorithm.